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Research on Capsule Network Based on Attention Mechanism Cover
By: Yan Jiao,  Li Zhao and  Hexin Xu  
Open Access
|Feb 2022

Figures & Tables

Figure 1.

The original capsule network
The original capsule network

Figure 2.

CBAM Module
CBAM Module

Figure 3.

Channel Attention Module
Channel Attention Module

Figure 4.

Spatial Atttion Module
Spatial Atttion Module

Figure 5.

Capsule network based on attention mechanism
Capsule network based on attention mechanism

j_ijanmc-2021-011_utab_001

ProcedureRouting algorithm
1:procedure ROUTING ( u ^ i j , i , l )
2:    for all capsule i in layer l and capsule j in layer (l + 1):bij ←0
3:    for r iterations do
4:        for all capsule i in layer l : ci ← softmax (bi )
5:        for all capsule j in layer (l +1) : sj i C i j u ^ i | j
6:        for all capsule j in layer (l +1) : vj ← squash (sj )
7:        for all capsule i in layer l and capsule j in layer (l + l)
8: return vj

NETWORK MODEL AND PARAMETERS

LayerParameters
Conv inputChannel:1; outPutChannel:256 kernel size= 9; stride=1
CBAM Channel:256
PrimaryCaps InputChannel:256;OutputCaps:32*6*6output_dim:8;kernel_size:9,stride:2
MgitCaps Inputeaps:32*6*6;out_put_caps:10

NETWORK MODEL ACCURACY

Net Work NameRouting NumberCBAM ModuleMax AccuracyFirst time
CapsNetl 1False99.70999908%133
CapsNetl_CBAM 1True99.66999817%100
CapsNet2 2False99.65000153%111
CapsNet2_CBAM 2True99.61000061%42
CapsNet3 3False99.69000244%131
CapsNet3_CBAM 3True99.62002324%82
CapsNet4 4False99.59999847%141
CapsNet4_CBAM 4True99.55999756%86
CapsNet5 5False99.65000153%125
Language: English
Page range: 1 - 8
Published on: Feb 21, 2022
Published by: Xi’an Technological University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2022 Yan Jiao, Li Zhao, Hexin Xu, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution 4.0 License.